Deep Learning for Epidemiologists: An Introduction to Neural Networks
It addresses the lack of formal training in deep learning for epidemiologists, facilitating interdisciplinary dialogue to improve healthcare applications.
This article introduces deep learning fundamentals to epidemiologists, aiming to bridge the knowledge gap by reviewing core concepts, architectures, and model processes to enable critical evaluation of medical applications.
Deep learning methods are increasingly being applied to problems in medicine and healthcare. However, few epidemiologists have received formal training in these methods. To bridge this gap, this article introduces to the fundamentals of deep learning from an epidemiological perspective. Specifically, this article reviews core concepts in machine learning (overfitting, regularization, hyperparameters), explains several fundamental deep learning architectures (convolutional neural networks, recurrent neural networks), and summarizes training, evaluation, and deployment of models. We aim to enable the reader to engage with and critically evaluate medical applications of deep learning, facilitating a dialogue between computer scientists and epidemiologists that will improve the safety and efficacy of applications of this technology.